A variety of different medical treatments require monitoring of certain physiological parameters indicative of a patient's medical condition. Typically, the patient and/or a healthcare provider studies the monitored parameters and makes changes to the treatment as indicated by the analysis of the monitored parameters. As a general rule, the healthcare provider's ability to optimize the patient's treatment improves with increases in the amount of high quality data available for analysis. Larger quantities of data, however, become increasingly difficult to analyze and interpret, especially when relationships between different physiological parameters must be discerned to fully understand the patient's condition.
The treatment of diabetes, for example, involves a detailed analysis of a relatively large amount of data collected over a period of time. In addition to blood glucose (bG) measurements, the data may include information and measurements of A1c values, Albumin values, Albumin excretion values, body mass index values, blood pressure values, carbohydrate values, cholesterol values (total, HDL, LDL, ratio) creatinine values, fructosamine values, HbA1 values, height values, insulin dose values, insulin rate values, total daily insulin values, ketone values, microalbumin values, proteinuria values, heart rate values, temperature values, triglyceride values, and weight values, exercise, sleep, stress, etc. When considered together, relationships and trends between these various parameters emerge, and provide valuable insight to the healthcare provider for making adjustments to the patient's treatment regimen. It is, however, difficult to discern these relationships and trends by simply reviewing the raw data in, for example, tabular format.